Underwater navigation by aided light sensor

ABSTRACT

The present invention provides a navigation system for an unmanned underwater vehicle that includes at least two navigation sensors that provide latitude and longitude measurements of unequal accuracy, a processor and a memory. The memory stores a machine-readable set of instructions executable by the processor to process the latitude and longitude measurements received from the navigation sensors, and to estimate vehicle latitude and longitude with approximately equal accuracy.

FIELD OF THE INVENTION

The present invention relates to navigation. More particularly, thepresent invention relates to navigation for unmanned underwater vehiclesusing sensors that provide measurements of latitude and longitude ofunequal accuracy.

BACKGROUND OF THE INVENTION

Traditional navigation systems for manned vessels, such as inertialnavigation systems, Doppler-aided inertial navigation systems, GPS-aidedinertial navigation systems, etc., are highly accurate but relativelylarge, heavy, expensive, and power-hungry. Conversely, a navigationsystem for an unmanned underwater vehicle (UUV) is preferably small insize, lightweight and inexpensive, and should consume much less powerthan a traditional inertial navigation system in order to facilitatelong duration, autonomous missions.

For example, the navigational load may be apportioned between a moreaccurate navigation system, to be used during a small portion of the UUVmission, such as a search phase, and a less accurate navigation system,to be used during the remaining portion of the UUV mission, such as thetransit phase(s). Of course, the UUV navigation system used during thetransit phase must satisfy particular navigational accuracyrequirements, such as, for example, 10 nautical miles (nm) (1−σ) in bothlatitude and longitude.

While inexpensive sensors have been produced which can measure eitherlatitude or longitude to an accuracy of about 10 nm, an inexpensive,compact, light weight, low power navigation system has yet to bedeveloped which can estimate both latitude and longitude to the sameaccuracy, i.e., about 10 nm.

For example, in recent years wildlife scientists have attachedinexpensive, miniature archival light sensors to fish in order to recordtheir movements in the open ocean. These sensors typically recordunderwater light level, depth and temperature data. Contemporary sensorscan sense light at approximately 300 meter depths and offer up to 16 MBof data storage. Latitude and longitude can be determined from the lightmeasurements, depth measurements and an on-board clock. Accuraciesdepend on total daily sensor motion, depth, time of the year, and cloudcover patterns but can be, at the one sigma level, approximately 10 nm(about 20 km) in longitude and 130 nm (about 260 km) in latitude. Theselight sensors are typically better at measuring longitude than latitude,since the estimation of local noon (i.e., longitude) is more accuratethan the estimation of the length of the day (i.e., latitude).

Inexpensive microelectromechanical system (MEMS) devices includeminiature accelerometers, angular rate sensors, gyroscopes, etc., whichmay be combined to form a MEMS inertial measurement unit (IMU). MEMSgyroscopes offer low cost, compact size, low weight, and low powerconsumption, but are far less accurate than fiber-optic or ring-lasergyros. Unfortunately, when several MEMS gyroscopes, or angular ratesensors, are combined with several MEMS accelerometers, and otherelectronics, to form a MEMS IMU, the cost, weight, size and powerconsumption increase substantially over a single MEMS gyroscope.

Thus, a need remains for an inexpensive, small, lightweight, low powerunderwater navigation system that can estimate both latitude andlongitude to the same accuracy, such as, for example, 10 nm.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a navigation system for anunmanned underwater vehicle that includes at least two navigationsensors that provide latitude and longitude measurements of unequalaccuracy, a processor and a memory. The memory stores a machine-readableset of instructions executable by the processor to process the latitudeand longitude measurements received from the navigation sensors, and toestimate vehicle latitude and longitude with approximately equalaccuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other advantages of this invention will become moreapparent by the following description of invention and the accompanyingdrawings.

FIG. 1 is a schematic diagram depicting a navigation system inaccordance with embodiments of the present invention.

FIG. 2 is a schematic diagram depicting a light sensor in accordancewith embodiments of the present invention.

FIG. 3 illustrates an idealized plot of normalized light sensor ambientlight levels vs. time according to an embodiment of the presentinvention.

FIG. 4 depicts a representation of the Earth spinning about its axis,along with the orientation of a MEMS gyroscope with respect to theEarth's rotation vector, in accordance with an embodiment of the presentinvention.

FIG. 5 is a flowchart depicting a navigation method for an underwatervehicle, in accordance with embodiments of the present invention.

DETAILED DESCRIPTION

The present invention provides an inexpensive, compact, low weight, lowpower navigation system which uses relatively inexpensive navigationsensors to estimate the position of an underwater vehicle. The expectedlow cost, small size, light weight, and low power characteristics of thepresent invention are very desirable for long duration autonomous UUVmissions. For example, the present invention may be used for travel to,and from, a general search area, while a more accurate (and powerhungry) navigation system may be used during the search.

FIG. 1 is a schematic diagram depicting a navigation system 100 for anunderwater vehicle, in accordance with embodiments of the presentinvention. Navigation system 100 includes a plurality of relativelyinexpensive navigation sensors, such as, for example, light sensor 120and MEMS gyroscope 130, that provide measurements of latitude andlongitude of unequal accuracy, respectively. Navigation system 100 alsoincludes a processor 110 and a memory 112 that stores a set of machinereadable instructions executable by the processor 110 to processlatitude and longitude measurements, received from the navigationsensors, in order to accurately determine latitude and longitude. Inother embodiments, additional navigation sensors may be included, suchas, for example, a MEMS Inertial Measurement Unit (IMU) 140, an altitudesonar 150 and bathymetric database 162, a Global Positioning System(GPS) 160, etc.

The processor 110 is coupled to the light sensor 120 and the MEMSgyroscope 130, as well as to the memory 112. The processor 110 is alsocoupled to an I/O port 114 in order to communicate with other componentsor subsystems of the vehicle, such as, for example, a guidance andcontrol subsystem, a communications subsystem, a payload, etc.Alternatively, processor 110 may perform various aspects of thesesubsystem functionalities, such as, for example, guidance and control,etc. The I/O port 114 may support various reference signals, data bussignals, etc. In one embodiment, the light sensor 120 and MEMS gyroscope130 are coupled directed to the processor 110, while in anotherembodiment, the light sensor 120 and MEMS gyroscope 130 are coupled tothe processor 110 through a data bus 116.

The light sensor 120 provides latitude and longitude data to theprocessor 110. An exemplary light sensor is the LTD 2350 GeolocationTag, manufactured by Lotek Wireless Inc. (http://www.lotek.com).

FIG. 2 depicts a light sensor 220 in accordance with embodiments of thepresent invention. The light sensor 220 includes a microprocessor 221coupled to a memory 222, an I/O port 223, a light detector 224 and apressure sensor 225. Preferably, the light sensor 220 receives a realtime clock signal from an external source through I/O port 223, such as,for example, a frequency standard (not shown), a GPS receiver, etc.Advantageously, a common time reference signal, e.g., 1 PPS, may bedistributed to all of the components of the navigation system 100, asrequired. Greenwich Mean Time (GMT) time and date messages may also beprovided to the light sensor 220 through the I/O port 223.Alternatively, a temperature sensor 226 and a temperature-compensated,crystal-controlled real time clock 227, synchronized to GMT, may becoupled to the microprocessor 221. In this embodiment, the data providedby the temperature sensor 226 may be used to compensate the real timeclock 227.

The light detector 224 includes a photodiode, for example, to detectambient light levels in the water surrounding the vehicle. In order tomaximize the depth at which light may be detected by the light detector224, a filter may be used to limit the optical spectrum to a narrowrange of frequencies, such as, for example, a bandpass filter centeredat approximately 460 nm to 470 nm (i.e., the blue-green region). Themicroprocessor 221 periodically acquires ambient light levelmeasurements from the light detector 224, from which the local sunrise,sunset, noon, midnight and day length are calculated. From these data,longitude is determined based on the time difference between local noonand GMT, while latitude is determined based on the day length and Juliandate.

FIG. 3 illustrates an idealized plot 300 of normalized ambient lightlevels vs. time for a light sensor 120 located at a constant, arbitrarydepth, assuming no cloud cover. During the evening hours, the ambientlight level is at a minimum, while during the daylight hours, theambient light level is at a maximum. The positively and negativelysloped regions represent the transition periods of sunrise and sunset,respectively. A threshold for each transition region is used todetermine the time of sunrise 302 and the time of sunset 306. Thethreshold 312 may be the same for each region, as depicted in FIG. 3, orthe thresholds may be different in order to place each one within thesteepest portion of their respective region. The day length 310 isdetermined based on the difference between the sunrise 302 and sunset306 times, while noon 304 is determined based on the average of thesunrise 302 and sunset 306 times.

In this example, noon 304 occurs at 12:00 pm GMT, which corresponds to alongitude of 0° 0′ 0″. The day length is 12 hours, which, generally,infers a latitude near the equator. However, during the solar equinox,for example, latitude depends very weakly on day length. Clearly,latitude determinations based on ambient light levels are far lessaccurate than longitude determinations. Furthermore, if the vehicleexperiences changes in depth over the day, then the ambient light levelsmust be corrected accordingly. A simple exponential correaction, basedon the data provided by the pressure sensor 225, may be employed.

The MEMS gyroscope 130 provides angular rate data about two orthogonalinput axes, I₁ and I₂, to the processor 110. An exemplary MEMS gyroscopeis the G-2000 Dynamically Tuned Gyroscope, manufactured by NorthropGrumman Corp., (http://www.nsd.es.northropgrumman.com/), having an inputrate of 200 deg/sec and drift of 0.6 deg/hr.

FIG. 4 depicts a representation of the Earth, the Earth's rotationvector 408 and a vertically-oriented, stationary MEMS gyroscope 420 atthe North Pole and the Equator. The two input axes of the MEMS gyroscope420, I₁ and I₂, form a response plane which, for any given latitude,includes a projection, i.e., a vector component, of the Earth's rotationvector 408. For example, if a vertically-oriented, stationary MEMSgyroscope 420 is located at an equatorial position 404 (i.e., 0°latitude), the Earth's rotation vector 408 will lie completely withinthe response plane defined by the input axes I₁ and I₂. Thus, theEarth's rotation vector 408 may be easily measured by the MEMS gyroscope420, and the magnitude of this maximum value may be determined bycombining the responses of the input axis I₁ and I₂ in vector fashion,i.e., the square root of the sum of the squares.

Similarly, if the vertically-oriented, stationary MEMS gyroscope 420 islocated at a north pole position 402 (i.e., 90° latitude), the Earth'srotation vector 408 will lie completely outside the response planedefined by the input axes I₁ and I₂. In other words, the Earth'srotation vector 408 is perpendicular to the response plane. In thiscase, the Earth's rotation vector 408 will not be measured by the MEMSgyroscope 420, and a minimum value, approaching zero, may be determined.Advantageously, any arbitrary latitude may be determined by avertically-oriented, non-rotating gyroscope once the minimum and maximumresponse magnitudes are known. In a preferred embodiment, if thevertically-oriented, stationary MEMS gyroscope 420 is located at unknownposition 406, the vector magnitude of the responses of the input axis I₁and I₂ is calculated and then divided by the maximum value of theEarth's rotation vector 408 determined apriori. The resulting quantityis then converted to latitude by applying an inverse cosine function.Alternatively, a look-up table, containing, for example, inverse cosinevalues, may be consulted. Assuming minimal error due to verticalorientation and rigid body rotation, a MEMS gyroscope 420 can measurelatitude to approximately 10 nm at the equator (1-σ).

Of course, it is not necessary to travel to the equator and the northpole to measure the maximum and minimum values of the Earth's rotationvector 408 using the MEMS gyroscope 420. At any given latitude, once thedirection of the local vertical is determined, the MEMS gyroscope 420can be manipulated such that the Earth's rotation vector 408 is firstnormal to the response plane and then perpendicular to the responseplane. Angular rate measurements are taken using the MEMS gyroscope 420,at each orientation, and the minimum and maximum values of the Earth'srotation vector 408 are recorded. The direction of local vertical may bedetermined, for example, by three orthogonal accelerometers placed atknown orientations with respect to the MEMS gyroscope 420. For example,the accelerometers and MEMS gyroscope 420 could be rotated as a unit sothat only one accelerometer indicates the earth's gravity with a nullreading from each of the other two accelerometers. The direction oflocal vertical then lies along the one accelerometer indicating theearth's gravity. A stable platform may be used to increase the accuracyof the measurements.

The latitude estimate, provided by the light sensor 120, is combinedwith the longitude estimate, calculated by the processor 110, todetermine the position of the vehicle. In one embodiment, a number oflatitude and longitude estimates may be curve fit, by standardleast-squares techniques, to improve system accuracy and providenavigational information between measured points. The “n” measurementsare determined by a “sliding data window,” i.e., the “n” measurementsare always the latest “n” measurements available, so that the oldestmeasurement is dropped as soon as a new measurement is available.

While the present invention is directed to a navigation system for anunderwater vehicle, a typical UUV will also include a guidance & controlsystem to autonomously pilot the vehicle to its intended destination.Accordingly, various sources of error with the navigation system 100 maybe reduced through the use of additional information provided by theguidance & control system or by additional equipment which augment thebasic functionality of the navigation system 100 described above. Forexample, the guidance & control system may include an inertialmeasurement unit (IMU) to determine the attitude and motion of thevehicle. A typical IMU includes three orthogonal accelerometers and atleast three orthogonal angular rate sensors, although gyroscopic-basedIMUs typically include at least one redundant response axis. Theattitude and angular motion of the vehicle may be provided to thenavigation system 100 so that the processor 110 can correct themicroelectromechanical gyroscope data prior to the determination oflatitude. If the vehicle mission requires accurate navigation on-site,then a traditional IMU may already be incorporated into the guidance &control system or payload, such as, for example, the Honeywell HG9900IMU, which includes two digital laser gyros, three accelerometers andattendant electronics (http://www.honeywell.com/). The traditional IMUcould be turned on and used sparingly (so as to conserve power) toprovide latitude and longitude correactions to the navigation system100.

If an IMU is not included within the guidance & control system, thenadditional components may be incorporated into the navigation system toimprove the accuracy of the latitude and longitude measurements. In oneembodiment, a MEMS IMU 140 may be coupled to the processor 110 toprovide acceleration and angular rate data. The MEMS IMU 140 includesthree orthogonal MEMS accelerometers, such as, for example, three AnalogDevices ADXL105 single axis iMEMS accelerometers, and three orthogonalMEMS angular rate sensors, such as, for example, three Analogue DevicesADXRS150 single chip angular rate sensors (http://www.analog.com/). TheMEMS IMU 140 uses much less power than a traditional IMU, and can befurther deactivated, periodically, to conserve power. The IMU can alsoprovide a dead-reckoning capability, allowing the navigation system 100to estimate longitude during periods of extensive cloud cover, storms,etc. In a related embodiment, the MEMS IMU 140 may already include themicroelectromechanical gyroscope 130 as an integral component.

In a further embodiment, a small altitude sonar 150 may be incorporatedinto the navigation system 100 to allow terrain-matching techniques tobe used to increase navigational accuracy in areas where the bottomtopography was mapped. In areas with significant terrain features, theimprovement in accuracy with the addition of terrain-matching may be aslarge as a factor of two, producing approximately 5 nm accuracy inlatitude and longitude. An exemplary sonar altimeter is the Model 1007Hydroacoustic Altimeter Head, manufactured by Konsberg Maritime(http://www.km.kongsberg.com/), which is coupled to an appropriate sonarsignal processor, such as the processor 110, a Konsberg MS 1000 scanningsonar processor, etc. Bathymetric data 162 are loaded into memory 112,preferably in a read-only partition, or stored in a separate ROM. Manydifferent terrain-following navigation techniques are known; oneexemplary method is disclosed within “Terrain-Relative Navigation forAutonomous Underwater Vehicles,” D. E. Di Massa and W. K. Stewart Jr.,Oceans '97, MTS/IEEE Conference Proceedings, Vol. 1, pp. 541-546,October 1997, which is incorporated herein by reference in its entirety.

In an embodiment, a least-squares curve fitting technique may be appliedto the series of measured positions to improve accuracy by about afactor of two, producing roughly 2 nm accuracy in both latitude andlongitude when both terrain-matching and least squares data reductiontechniques are used.

If dictated by mission requirements, periodic surfacings for precise GPSfixes could be used to improve system accuracy by replacing theestimated latitude and longitude with very accurate GPS-based positioninformation. Accordingly, a GPS system 160, including a receiver andantenna, may be coupled to the processor 110. An exemplary receiver isthe GPS 25, manufactured by Garmin Ltd. (http://www.garmin.com/).Subsequent estimates of latitude and longitude by the light sensor 120and the microelectromechanical gyroscope 130 may be incorporated intothe GPS-based position using a Kalman Filter, for example. Indeed, inone embodiment, the navigation system 100 includes several sources ofposition information having different accuracies and update periods,including the light sensor 120, the microelectromechanical gyroscope130, the MEMS IMU 140, the altitude sonar 150 and bathymetric database162, and the GPS 160. Preferably, a Kalman Filter would blend these datainto the most accurate position estimate for the navigation system 100.Of course, a Kalman Filter would work quite well with any sensor suite.

A Doppler sonar (not shown) is another navigational aid that may beemployed within navigational system 100. The Doppler sonar providesvelocity information, which may also be integrated over time to providelatitude and longitude estimates for the Kalman Filter.

FIG. 5 is a flowchart depicting a navigation method for an underwatervehicle, in accordance with embodiments of the present invention.

The light sensor 120 periodically transmits data to the processor 110,either autonomously or in response to a request from the processor 110.These data may include latitude, longitude, depth and temperature, aswell as other information, such as, for example, raw ambient lightlevel, measurement time, status telemetry, etc. The processor 110processes (502) these data, extracts the longitude information andestimates (504) the longitude based on the extracted longitudeinformation. In one embodiment, the data include a single longitudevalue, and the processor 110 simply uses the single longitude value asthe estimated longitude. In another embodiment, the data include severallongitude values, and the processor 110 may estimate (504) the longitudeby averaging all of the longitude values together, by calculating themedian value, etc.

Similarly, the microelectromechanical gyroscope 130 periodicallytransmits data to the processor 110, either autonomously or in responseto a request from the processor 110. These data include angular rateinformation for the two orthogonal response axes, as well as otherinformation, such as, for example, measurement time, status telemetry,etc. The processor 110 processes (510) these data, extracts the angularrate information, calculates (512) the magnitude of the earth rotationvector, normalizes (514) the earth rotation vector magnitude andestimates (516) the latitude based on the normalized magnitude of theearth rotation vector. In a preferred embodiment, the earth rotationvector is calculated by vectorially combining the two orthogonal angularrate vectors from which the magnitude is calculated (512), the earthrotation vector magnitude is normalized (514) using the maximum earthrotation vector magnitude and the latitude is estimated (516) byapplying an inverse cosine function to the normalized earth rotationvector magnitude.

In one embodiment, the data include only two angular rate values, onefor each orthogonal axis, and the processor 110 simply uses the twoorthogonal angular rate values, as described above. In anotherembodiment, the data include several angular rate values, and theprocessor 110 processes (510) the angular rate values first by averagingall of the angular rate values together for each orthogonal axis, bycalculating the median value for each orthogonal axis, etc.

The position of the vehicle is determined (518) by combining theestimated latitude and the estimated longitude. As described above, inone embodiment, a number of latitude and longitude estimates may becurve fit, by applying (520) a least-squares curve fit, to improvesystem accuracy and provide navigational information between measuredpoints. Alternatively, a Kalman Filter may be applied (530) to theestimated latitude and longitude data, particularly if more than onedata source is available for either component. Accordingly, in oneembodiment, GPS data is processed (540) to extract latitude, longitudeand time data, and blended with the data provided by the light sensor120 and the microelectromechanical gyroscope 130 by applying (530) theKalman Filter. Similarly, in a further embodiment, altitude sonar datais processed (550) to extract altitude, combined with time data andmatched (552) to a terrain map 162 to determine latitude and longitudeand then blended with the other sources of latitude and longitudeinformation by applying (530) the Kalman Filter.

While this invention has been described in conjunction with specificembodiments thereof, many alternatives, modifications and variationswill be apparent to those skilled in the art. Accordingly, the preferredembodiments of the invention as set forth herein, are intended to beillustrative, not limiting. Various changes may be made withoutdeparting from the true spirit and full scope of the invention as setforth herein.

1. A navigation system for an unmanned underwater vehicle, comprising:at least two navigation sensors that provide latitude and longitudemeasurements of unequal accuracy; a processor; and a memory storing amachine-readable set of instructions executable by the processor to:process the latitude and longitude measurements received from thenavigation sensors, and estimate vehicle latitude and longitude withapproximately equal accuracy.
 2. The navigation system according toclaim 1, wherein the navigation sensors include: a first navigationsensor to provide latitude data and longitude data, the longitude databeing more accurate than the latitude data; and a second navigationsensor to provide at least latitude data, the latitude data being asaccurate as the first navigation sensor longitude data.
 3. Thenavigation system according to claim 2, wherein the first navigationsensor longitude data is an order of magnitude more accurate than thefirst navigation sensor latitude data.
 4. The navigation systemaccording to claim 2, wherein the second navigation sensor provideslongitude data that is less accurate than the second navigation sensorlatitude data.
 5. The navigation system according to claim 1, whereinthe machine-readable set of instructions are further executable by theprocessor to estimate vehicle latitude and longitude using aleast-squares curve fit.
 6. The navigation system according to claim 1,wherein the machine-readable set of instructions are further executableby the processor to estimate vehicle latitude and longitude using aKalman Filter.
 7. The navigation system of claim 1, wherein thenavigation sensors include an altitude sensor, and wherein themachine-readable set of instructions are further executable by theprocessor to estimate vehicle latitude and longitude by matching thealtitude data to a bathymetric map database.
 8. The navigation system ofclaim 1, wherein the navigation sensors include a GPS receiver toprovide latitude and longitude data of similar accuracy, and wherein themachine-readable set of instructions are further executable by theprocessor to estimate vehicle latitude and longitude based on the GPSdata.
 9. The navigation system of claim 1, wherein the navigationsensors include a MEMS inertial measurement unit to provide threedimensional position data of similar relative accuracy, and wherein themachine-readable set of instructions are further executable by theprocessor to estimate vehicle latitude and longitude based on the MEMSinertial measurement unit data.
 10. An underwater navigation system,comprising: a light sensor to provide at least longitude data; amicroelectromechanical gyroscope to provide angular rate data about twoorthogonal axes; and a processor, coupled to a memory, the light sensorand the microelectromechanical gyroscope, adapted to estimate positionbased on the light sensor data and the microelectromechanical gyroscopeangular rate data.
 11. The underwater navigation system of claim 10,wherein the position estimate includes a longitude estimate provided bythe light sensor and a latitude estimate determined from themicroelectromechanical gyroscope angular rate data.
 12. The underwaternavigation system of claim 10, further comprising three orthogonalmicroelectromechanical accelerometers, coupled to the processor, toprovide acceleration data.
 13. The underwater navigation system of claim10, wherein the processor is adapted to estimate position using aleast-squares curve fit technique.
 14. The underwater navigation systemof claim 10, wherein the processor is adapted to estimate position usinga Kalman Filter.
 15. The underwater navigation system of claim 10,further comprising: a bathymetric map database; and an altitude sonar toprovide altitude data, wherein the processor is further adapted to matchthe altitude data to the bathymetric data to estimate position.
 16. Theunderwater navigation system of claim 10, further comprising a GPSreceiver to provide latitude and longitude data, wherein the processoris further adapted to estimate position based on the light sensor data,the microelectromechanical gyroscope data and the GPS data.
 17. Theunderwater navigation system of claim 10, further comprising amicroelectromechanical inertial measurement unit, including threeorthogonal angular rate sensors to provide angular rate data, and threeorthogonal accelerometers to provide acceleration data, wherein theprocessor is further adapted to estimate position based on the lightsensor data, the microelectromechanical gyroscope data and themicroelectromechanical inertial measurement unit data.
 18. A navigationmethod for an underwater vehicle, comprising: processing light sensordata to estimate longitude; processing microelectromechanical gyroscopeangular rate data to estimate latitude, including: calculating an earthrotation vector based on the microelectromechanical gyroscope angularrate data, normalizing the magnitude of the earth rotation vector, andestimating latitude based on the normalized earth rotation vectormagnitude; and determining the position of the vehicle based on theestimated latitude and the estimated longitude.
 19. The underwaternavigation method of claim 18, wherein the microelectromechanicalgyroscope angular rate data includes two orthogonal angular rate vectorsand the earth rotation vector is calculated by vectorially combining thetwo orthogonal angular rate vectors.
 20. The underwater navigationmethod of claim 18, wherein the earth rotation vector magnitude isnormalized by a maximum earth rotation vector magnitude determinedapriori.
 21. The underwater navigation method of claim 18, wherein thelatitude is estimated by applying an inverse cosine function to thenormalized earth rotation vector magnitude.
 22. The underwaternavigation method of claim 18, further comprising applying a leastsquares curve fit to the estimated latitude and longitude.
 23. Theunderwater navigation method of claim 18, further comprising applying aKalman Filter to the estimated latitude and longitude.
 24. Theunderwater navigation method of claim 18, wherein said processingmicroelectromechanical gyroscope angular rate data further comprisescorrecting the microelectromechanical gyroscope angular rate data basedon vehicle attitude.
 25. The underwater navigation method of claim 18,further comprising: processing GPS data to provide latitude andlongitude; and applying a Kalman Filter to the estimated light sensorlongitude, the estimated microelectromechanical gyroscope latitude andthe GPS latitude and longitude.
 26. The underwater navigation method ofclaim 18, further comprising: processing altitude sonar data to providea vehicle altitude; matching a terrain map to the altitude data toestimate latitude and longitude; and applying a Kalman Filter to theestimated light sensor longitude, the estimated microelectromechanicalgyroscope latitude and the map-matched latitude and longitude.